1. Field of the Invention
The present invention is directed to a computerized method and system for analysis of chest radiographs to determine the presence or absence of interstitial lung disease and if interstitial disease is discovered, to classify the abnormalities on the basis of detected parameters related to the characteristics unique to a given abnormality type.
2. Discussion of the Related Art
In order to detect and characterize interstitial disease, there has recently been developed a computerized scheme, based on Fourier analysis techniques, for quantifying lung textures in digital chest radiographs. Such a method is disclosed in U.S. Pat. Nos. 4,839,807 and 4,851,984 both to Doi et al, the inventors of the present application. In this method, a conventional posterior-anterior (PA) chest radiograph is digitized with a drum scanner system employing a 0.1 millimeter pixel size and a 10-bit gray scale. Approximately 20 square regions of interest (ROIs) with a 64.times.64 matrix size are selected from the intercostal spaces. Manually interactive operations are needed in the ROI selection for the avoidance of ribs. A non-uniform background trend caused by the gross anatomy of the lung and chest wall is corrected by fitting a 2-dimensional surface to the original image in an ROI and subtraction of the fitted surface from the original image. Such a surface-fitting technique facilitates the determination of fluctuating patterns of the underlying lung texture for subsequent analysis and processing by a computer.
The root mean square (RMS) variation, also referred to as R, and the first moment of the power spectrum, commonly referred to as M, are then determined, by use of the two-dimensional Fourier transform, as quantitative measures of the magnitude and coarseness (or fineness), respectively, of the lung texture. The two-dimensional Fourier transformed data are defined in terms of a function T (u,v) where u and v are spatial frequencies in a cartesian coordinate system. The function T (u,v) is band-pass filtered by another function known in the art as the human visual response V (u,v) as a means of suppressing low frequency and high frequency components, in order to enhance differences between normal and abnormal lungs.
From the filtered data (T (u,v), V (u,v)) the two texture measures R and M are obtained for each ROI. The ROIs are then classified as normal or abnormal on the basis of a comparison of these texture measures and a data base derived from clinical cases. The data base is obtained by determining average R and M values from lungs which were predetermined to be normal or abnormal. The normal lungs on average showed R values which were lower than those for abnormal lungs and M values which were higher. The results are displayed on a CRT monitor, providing a "second opinion" as an aid to radiologists in their interpretation.
This previous method was useful for distinguishing relatively obvious abnormal lung textures due to nodular, reticular and reticulo-nodular patterns. However, it became apparent in recent studies (Katsuragawa S, Doi K, MacMahon H, Nakamori N. Sasaki Y, Fennessy J. J.: quantitative analysis of lung texture in the ILO pneumoconioses standard radiographs, Radiographics Vol. 10, pp. 257-269, 1990) that the two texture measures R and M are not adequate to distinguish subtle abnormal lung textures due to various patterns, because the texture measures determined for round opacities were almost identical to those for irregular opacities. This result indicates the need for a new approach to the quantization and distinction of subtle texture patterns in chest images.